def __get_label__(args, params):
with tf.Session() as sess:
# Obtain the test labels
dataset = mnist_dataset.train(args.data_dir)
dataset = dataset.map(lambda img, lab: lab)
dataset = dataset.batch(params.train_size)
labels_tensor = dataset.make_one_shot_iterator().get_next()
labels = sess.run(labels_tensor)
return labels
def train_embedding_fn(data_dir, params):
"""Compute embedding of the training set,remember:No shuffle!!! Need to keep order with labels
Args:
data_dir: (string) path to the data directory
params: (Params) contains hyperparameters of the model (ex: `params.num_epochs`)
"""
dataset = mnist_dataset.train(data_dir)
dataset = dataset.batch(params.batch_size)
dataset = dataset.prefetch(
1) # make sure you always have one batch ready to serve
return dataset
def train_input_fn(data_dir, params):
"""Train input function for the MNIST dataset.
Args:
data_dir: (string) path to the data directory
params: (Params) contains hyperparameters of the model (ex: `params.num_epochs`)
"""
dataset = mnist_dataset.train(data_dir)
dataset = dataset.shuffle(params.train_size)
dataset = dataset.repeat(params.num_epochs) # repeat for multiple epochs
dataset = dataset.batch(params.batch_size)
dataset = dataset.prefetch(
1) # make sure you always have one batch ready to serve
return dataset